SoroushVahidi/scidocs huggingface dataset:
Compacting Neural Network Classifiers via Dropout Training
(2016)

We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization, yet extracts a compact neural network for run-time efficiency. In the proposed method, we introduce a sparsity-inducing prior on the per unit dropout retention probability so that the optimizer can effectively prune hidden units during training. By changing the prior hyperparameters, we can control the size of the resulting network. We performed a systematic comparison of dropout compaction and competing methods on several realworld speech recognition tasks and found that dropout compaction achieved comparable accuracy with fewer than 50% of the hidden units, translating to a 2.5x speedup in run-time.
Online search of overlapping communities
(2013)

A great deal of research has been conducted on modeling and discovering communities in complex networks. In most real life networks, an object often participates in multiple overlapping communities. In view of this, recent research has focused on mining overlapping communities in complex networks. The algorithms essentially materialize a snapshot of the overlapping communities in the network. This approach has three drawbacks, however. First, the mining algorithm uses the same global criterion to decide whether a subgraph qualifies as a community. In other words, the criterion is fixed and predetermined. But in reality, communities for different vertices may have very different characteristics. Second, it is costly, time consuming, and often unnecessary to find communities for an entire network. Third, the approach does not support dynamically evolving networks. In this paper, we focus on online search of overlapping communities, that is, given a query vertex, we find meaningful overlapping communities the vertex belongs to in an online manner. In doing so, each search can use community criterion tailored for the vertex in the search. To support this approach, we introduce a novel model for overlapping communities, and we provide theoretical guidelines for tuning the model. We present several algorithms for online overlapping community search and we conduct comprehensive experiments to demonstrate the effectiveness of the model and the algorithms. We also suggest many potential applications of our model and algorithms.
Virtual reality exposure therapy.
(1997)

It has been proposed that virtual reality (VR) exposure may be an alternative to standard in vivo exposure. Virtual reality integrates real-time computer graphics, body tracking devices, visual displays, and other sensory input devices to immerse a participant in a computer-generated virtual environment. Virtual reality exposure is potentially an efficient and cost-effective treatment of anxiety disorders. VR exposure therapy reduced the fear of heights in the first controlled study of virtual reality in treatment of a psychiatric disorder. A case study supported the efficacy of VR exposure therapy for the fear of flying. The potential for virtual reality exposure treatment for these and other disorders is explored, and therapeutic issues surrounding the delivery of VR exposure are discussed.
Virtual reality exposure therapy for Vietnam veterans with posttraumatic stress disorder.
(2001)

BACKGROUND
Virtual reality (VR) integrates real-time computer graphics, body-tracking devices, visual displays, and other sensory input devices to immerse a participant in a computer-generated virtual environment that changes in a natural way with head and body motion. VR exposure (VRE) is proposed as an alternative to typical imaginal exposure treatment for Vietnam combat veterans with posttraumatic stress disorder (PTSD).


METHOD
This report presents the results of an open clinical trial using VRE to treat Vietnam combat veterans who have DSM-IV PTSD. In 8 to 16 sessions, 10 male patients were exposed to 2 virtual environments: a virtual Huey helicopter flying over a virtual Vietnam and a clearing surrounded by jungle.


RESULTS
Clinician-rated PTSD symptoms as measured by the Clinician Administered PTSD Scale, the primary outcome measure, at 6-month follow-up indicated an overall statistically significant reduction from baseline (p = .0021) in symptoms associated with specific reported traumatic experiences. All 8 participants interviewed at the 6-month follow-up reported reductions in PTSD symptoms ranging from 15% to 67%. Significant decreases were seen in all 3 symptom clusters (p < .02). Patient self-reported intrusion symptoms as measured by the Impact of Event Scale were significantly lower (p < .05) at 3 months than at baseline but not at 6 months, although there was a clear trend toward fewer intrusive thoughts and somewhat less avoidance.


CONCLUSION
Virtual reality exposure therapy holds promise for treating PTSD in Vietnam veterans.
Influence of emotional engagement and habituation on exposure therapy for PTSD.
(1998)

This study examined 2 process variables, emotional engagement and habituation, and outcome of exposure therapy for posttraumatic stress disorder. Thirty-seven female assault victims received treatment that involved repeated imaginal reliving of their trauma, and rated their distress at 10-min intervals. The average distress levels during each of 6 exposure sessions were submitted to a cluster analysis. Three distinct groups of clients with different patterns of distress were found: high initial engagement and gradual habituation between sessions, high initial engagement without habituation, and moderate initial engagement without habituation. Clients with the 1st distress pattern improved more in treatment than the other clients. The results are discussed within the framework of emotional processing theory, emphasizing the crucial role of emotional engagement and habituation in exposure therapy.
Virtual Reality Exposure Therapy for World Trade Center Post-traumatic Stress Disorder: A Case Report
(2002)

Done properly by experienced therapists, re-exposure to memories of traumatic events via imaginal exposure therapy can lead to a reduction of Post-traumatic Stress Disorder (PTSD) symptoms. Exposure helps the patient process and habituate to memories and strong emotions associated with the traumatic event: memories and emotions they have been carefully avoiding. But many patients are unwilling or unable to self-generate and re-experience painful emotional images. The present case study describes the treatment of a survivor of the World Trade Center (WTC) attack of 9-11-01 who had developed acute PTSD. After she failed to improve with traditional imaginal exposure therapy, we sought to increase emotional engagement and treatment success using virtual reality (VR) exposure therapy. Over the course of six 1-h VR exposure therapy sessions, we gradually and systematically exposed the PTSD patient to virtual planes flying over the World Trade Center, jets crashing into the World Trade Center with animated explosions and sound effects, virtual people jumping to their deaths from the burning buildings, towers collapsing, and dust clouds. VR graded exposure therapy was successful for reducing acute PTSD symptoms. Depression and PTSD symptoms as measured by the Beck Depression Inventory and the Clinician Administered PTSD Scale indicated a large (83%) reduction in depression, and large (90%) reduction in PTSD symptoms after completing VR exposure therapy. Although case reports are scientifically inconclusive by nature, these strong preliminary results suggest that VR exposure therapy is a promising new medium for treating acute PTSD. This study may be examined in more detail at www.vrpain.com.
Emotional processing of fear: exposure to corrective information.
(1986)

In this article we propose mechanisms that govern the processing of emotional information, particularly those involved in fear reduction. Emotions are viewed as represented by information structures in memory, and anxiety is thought to occur when an information structure that serves as program to escape or avoid danger is activated. Emotional processing is denned as the modification of memory structures that underlie emotions. It is argued that some form of exposure to feared situations is common to many psychotherapies for anxiety, and that confrontation with feared objects or situations is an effective treatment. Physiological activation and habituation within and across exposure sessions are cited as indicators of emotional processing, and variables that influence activation and habituation of fear responses are examined. These variables and the indicators are analyzed to yield an account of what information must be integrated for emotional processing of a fear structure. The elements of such a structure are viewed as cognitive representations of the stimulus characteristic of the fear situation, the individual's responses in it, and aspects of its meaning for the individual. Treatment failures are interpreted with respect to the interference of cognitive defenses, autonomic arousal, mood state, and erroneous ideation with reformation of targeted fear structures. Applications of the concepts advanced here to therapeutic practice and to the broader study of psychopathology are discussed.
A Simple and Effective Unsupervised Word Segmentation Approach
(2011)

In this paper, we propose a new unsupervised approach for word segmentation. The core idea of our approach is a novel word induction criterion called WordRank, which estimates the goodness of word hypotheses (character or phoneme sequences). We devise a method to derive exterior word boundary information from the link structures of adjacent word hypotheses and incorporate interior word boundary information to complete the model. In light of WordRank, word segmentation can be modeled as an optimization problem. A Viterbistyled algorithm is developed for the search of the optimal segmentation. Extensive experiments conducted on phonetic transcripts as well as standard Chinese and Japanese data sets demonstrate the effectiveness of our approach. On the standard Brent version of BernsteinRatner corpora, our approach outperforms the state-ofthe-art Bayesian models by more than 3%. Plus, our approach is simpler and more efficient than the Bayesian methods. Consequently, our approach is more suitable for real-world applications.
Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition
(2018)

The gap between unstructured natural language and structured data makes it challenging to build a system that supports using natural language to query large knowledge graphs. Many existing methods construct a structured query for the input question based on a syntactic parser. Once the input question is parsed incorrectly, a false structured query will be generated, which may result in false or incomplete answers. The problem gets worse especially for complex questions. In this paper, we propose a novel systematic method to understand natural language questions by using a large number of binary templates rather than semantic parsers. As sufficient templates are critical in the procedure, we present a low-cost approach that can build a huge number of templates automatically. To reduce the search space, we carefully devise an index to facilitate the online template decomposition. Moreover, we design effective strategies to perform the two-level disambiguations (i.e., entity-level ambiguity and structure-level ambiguity) by considering the query semantics. Extensive experiments over several benchmarks demonstrate that our proposed approach is effective as it significantly outperforms state-of-the-art methods in terms of both precision and recall. PVLDB Reference Format: Weiguo Zheng, Jeffrey Xu Yu, Lei Zou, and Hong Cheng. Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition. PVLDB, 11 (11): 1373-1386, 2018. DOI: https://doi.org/10.14778/3236187.3236192.
Demand Characteristics in Assessing Motion Sickness in a Virtual Environment: Or Does Taking a Motion Sickness Questionnaire Make You Sick?
(2007)

The experience of motion sickness in a virtual environment may be measured through pre and postexperiment self-reported questionnaires such as the Simulator Sickness Questionnaire (SSQ). Although research provides converging evidence that users of virtual environments can experience motion sickness, there have been no controlled studies to determine to what extent the user's subjective response is a demand characteristic resulting from pre and posttest measures. In this study, subjects were given either SSQ's both pre and postvirtual environment immersion, or only postimmersion. This technique tested for contrast effects due to demand characteristics in which administration of the questionnaire itself suggested to the participant that the virtual environment may produce motion sickness. Results indicate that reports of motion sickness after immersion in a virtual environment are much greater when both pre and postquestionnaires are given than when only a posttest questionnaire is used. The implications for assessments of motion sickness in virtual environments are discussed.
The security of modern password expiration: an algorithmic framework and empirical analysis
(2010)

This paper presents the first large-scale study of the success of password expiration in meeting its intended purpose, namely revoking access to an account by an attacker who has captured the account's password. Using a dataset of over 7700 accounts, we assess the extent to which passwords that users choose to replace expired ones pose an obstacle to the attacker's continued access. We develop a framework by which an attacker can search for a user's new password from an old one, and design an efficient algorithm to build an approximately optimal search strategy. We then use this strategy to measure the difficulty of breaking newly chosen passwords from old ones. We believe our study calls into question the merit of continuing the practice of password expiration.
Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.
(2017)

We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.
Novel Six-Slot Four-Pole Axial Flux-Switching Permanent Magnet Machine for Electric Vehicle
(2017)

This paper proposes several novel 6-slot/4-pole axial flux-switching machine topologies for electric and hybrid electric vehicles. The proposed topologies include stator shifting, rotor shifting, and a combination of shifting both the stator and the rotor. The proposed topologies eliminate the even harmonics of the flux linkage that create unbalanced flux linkage in the traditional 6-slot/4-pole machine. Fundamental frequency will be reduced by 60% compared with a 12-slot/10-pole axial flux-switching machine without unbalanced flux linkage. An analytical method is used to find the offset angle between stator a-axis and the rotor pole axis to realize the elimination of even harmonics of the flux linkage. The analytical method is verified by finite-element analysis and experiments. The results confirm that the proposed novel topologies efficiently eliminate the even harmonics of the flux linkage in the 6-slot/4-pole machine.
The inconspicuous penis in children
(2015)

The term 'inconspicuous penis' refers to a group of anatomical abnormalities in which the penis looks smaller than is expected. Micropenis can be defined as 'true micropenis'—which results from a defect in the hypothalamic–pituitary–gonadal axis—and 'micropenis secondary to congenital anatomical anomalies of the surrounding and overlying structures'—also known as 'concealed penis'. The different forms of concealed penis include webbed penis, congenital megaprepuce and partially hidden penis caused by prepubic adiposity. This disorder can also have iatrogenic causes resulting from adhesions that are secondary to circumcision—this type of concealed penis is known as 'trapped penis'. However, in both groups, micropenis is defined as a stretched penile length that is at least 2.5 SD below the mean for the patient's age, but without any other penile defects. Patients with true micropenis can be managed with testosterone, which has demonstrated good penile elongation results in the long term. Surgery also has a pivotal role in reconstruction for elongating the penis and for correction of anatomical abnormalities in concealed penis.
Neural Network based Performance Tuning in Distributed Database Environment
(2013)

The self-tuning of database management systems has important advantages like efficient use of resources, improved performance, reduced total cost of ownership, improved customer satisfaction etc. To support large database driven web-applications with high scalability and availability, the data tier invariably will have distributed database systems. The self-tuning of database systems in a distributed database environment is most desirable as it not only improves the quality of service to the end user applications, but it also provides central control over the entire tuning process. In this paper, a novel technique that leverages the learning ability of the Artificial Neural Network(ANN) has been employed to estimate the extent of tuning required at each database location. Further, the estimated values are used to fine-tune the Database systems at every location from remote control panel. The proposed method has been tested under OLTP workload-type with database distributed on three nodes and the experimental results are validated with workload characteristics based selftuning method. The results show a significant performance improvement og 18.64% as compared to built in selftuning feature of the Database Management System. The self-tuning method not only makes the end-user applications more responsive, but it will also help improve the business prospects of the enterprise.
Characterization of soft errors caused by single event upsets in CMOS processes
(2004)

Radiation-induced single event upsets (SEUs) pose a major challenge for the design of memories and logic circuits in high-performance microprocessors in technologies beyond 90nm. Historically, we have considered power-performance-area trade offs. There is a need to include the soft error rate (SER) as another design parameter. In this paper, we present radiation particle interactions with silicon, charge collection effects, soft errors, and their effect on VLSI circuits. We also discuss the impact of SEUs on system reliability. We describe an accelerated measurement of SERs using a high-intensity neutron beam, the characterization of SERs in sequential logic cells, and technology scaling trends. Finally, some directions for future research are given.
Multitechnique paraphrase alignment: A contribution to pinpointing sub-sentential paraphrases
(2013)

This work uses parallel monolingual corpora for a detailed study of the task of sub-sentential paraphrase acquisition. We argue that the scarcity of this type of resource is compensated by the fact that it is the most suited type for studies on paraphrasing. We propose a large exploration of this task with experiments on two languages with five different acquisition techniques, selected for their complementarity, their combinations, as well as four monolingual corpus types of varying comparability. We report, under all conditions, a significant improvement over all techniques by validating candidate paraphrases using a maximum entropy classifier. An important result of our study is the identification of difficult-to-acquire paraphrase pairs, which are classified and quantified in a bilingual typology.
Automatic Model Generation from Documentation for Java API Functions
(2016)

Modern software systems are becoming increasingly complex, relying on a lot of third-party library support. Library behaviors are hence an integral part of software behaviors. Analyzing them is as important as analyzing the software itself. However, analyzing libraries is highly challenging due to the lack of source code, implementation in different languages, and complex optimizations. We observe that many Java library functions provide excellent documentation, which concisely describes the functionalities of the functions. We develop a novel technique that can construct models for Java API functions by analyzing the documentation. These models are simpler implementations in Java compared to the original ones and hence easier to analyze. More importantly, they provide the same functionalities as the original functions. Our technique successfully models 326 functions from 14 widely used Java classes. We also use these models in static taint analysis on Android apps and dynamic slicing for Java programs, demonstrating the effectiveness and efficiency of our models.
Practical graph isomorphism, II
(2014)

We report the current state of the graph isomorphism problem from the practical point of view. After describing the general principles of the refinement-individualization paradigm and proving its validity, we explain how it is implemented in several of the key programs. In particular, we bring the description of the best known program nauty up to date and describe an innovative approach called Traces that outperforms the competitors for many difficult graph classes. Detailed comparisons against saucy, Bliss and conauto are presented. Email addresses: bdm@cs.anu.edu.au (Brendan D. McKay), piperno@di.uniroma1.it (Adolfo Piperno). 1 Supported by the Australian Research Council. Preprint submitted to Elsevier — nautytraces2b — 11 December 2013 — ar X iv :1 30 1. 14 93 v1 [ cs .D M ] 8 J an 2 01 3